Adaptation Techniques in MIMO

ABSTRACT

A method of the multiple input multiple output feedback is disclosed. In accordance with an embodiment of the invention, the multiple input multiple output feedback method includes a receiver receiving a reference signal from a base station and calculating a signal to interference and noise ratio from the received reference signal. The method further includes determining a modulation and coding scheme based on the signal to interference and noise ratio and a receiver type.

This application is a continuation patent application of U.S. patentapplication Ser. No. 12/186,368 filed Aug. 5, 2008 in the name of SyedAon Mujtaba, et al. entitled “Adaptation Techniques in MIMO” whichclaims the benefit of Provisional Patent Application Ser. No.61/027,720, filed Feb. 11, 2008, and entitled “Procedures for RankAdaptation in MIMO at High Vehicular Speeds,” which application ishereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

This invention relates generally to wireless communication systems, andmore particularly to adaptation techniques in MIMO.

BACKGROUND

Multiple input multiple output (MIMO) is a radio communication techniquein which both a transmitter and a receiver use multiple antennas towirelessly communicate with one another. By using multiple antennas atthe transmitter and receiver, the spatial dimension may be takenadvantage of in a manner that improves overall performance of thewireless link.

MIMO may be performed as either an open loop or a closed loop technique.In open loop MIMO, a transmitter has no specific knowledge of thecondition of the channel before signals are transmitted to a receiver.In closed loop MIMO, on the other hand, channel-related information isfed back from the receiver to the transmitter to allow the transmitterto precondition transmit signals before they are transmitted to bettermatch the present channel state. However, at high vehicular speeds, thechannel ages very fast, imposing significant challenges on the wirelesscommunication system.

Hence, there is a general need for strategies to improve transmission inMIMO systems by adapting the link between the transmitter and thereceiver.

SUMMARY OF THE INVENTION

These and other problems are generally solved or circumvented, andtechnical advantages are generally achieved, by embodiments of thepresent invention.

Embodiments of the invention include feedback methods for feedback inmultiple input multiple output communication systems. In accordance withan embodiment of the invention, the multiple input multiple outputfeedback method comprises a receiver receiving a reference signal from abase station and calculating a signal to interference and noise ratiofrom the received reference signal. The method further comprisesdetermining a modulation and coding scheme based on the signal tointerference and noise ratio and a type of the receiver.

The foregoing has outlined rather broadly the features of an embodimentof the present invention in order that the detailed description ofembodiments of the invention that follows may be better understood.Additional features and advantages of embodiments of the invention willbe described hereinafter, which form the subject of the claims of theinvention. It should be appreciated by those skilled in the art that theconception and specific embodiments disclosed may be readily utilized asa basis for modifying or designing other structures or processes forcarrying out the same purposes of the present invention. It should alsobe realized by those skilled in the art that such equivalentconstructions do not depart from the spirit and scope of the inventionas set forth in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a wireless communication systemusing a MIMO based wireless system, in accordance with an embodiment ofthe present invention;

FIG. 2 illustrates a schematic of the link adaptation, in accordancewith an embodiment of the present invention;

FIG. 3 illustrates 3GPP-LTE MIMO modes used in various embodiments ofthe present invention;

FIG. 4 illustrates an embodiment of the invention applied to closed loopMIMO systems;

FIG. 5 illustrates an embodiment of the invention applied to open loopMIMO systems; and

FIGS. 6 a-6 c, illustrates an application of embodiments of theinvention in a MIMO communication system and illustrates the importanceof the receiver type, wherein the user is moving at high vehicular speedrelative to the base station.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. The figures aredrawn to clearly illustrate the relevant aspects of the embodiments andare not necessarily drawn to scale.

DETAILED DESCRIPTION

The making and using of the presently preferred embodiments arediscussed in detail below. It should be appreciated, however, that thepresent invention provides many applicable inventive concepts that canbe embodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

Adaptation techniques adapt the transmission parameters to takeadvantage of prevailing channel conditions. The fundamental parametersto be adapted include rank, modulation and coding levels. Adaptation isa key solution to increase the spectral efficiency of wirelesscommunication systems. Adaptation exploits the variations of thewireless channel (over time, frequency, and/or space) by dynamicallyadjusting certain key transmission parameters to the changingenvironmental and interference conditions observed between the basestation (Node B) and the subscriber (user end UE).

In practical implementations, the values for the transmission parametersare quantized and grouped together as a set of modes. An example of sucha set of modes includes a pairing of modulation level and coding rate.Since each such mode comprises a different data rate (expressed in bitsper second) and robustness level (minimum signal-to noise ratio (SNR)needed to activate the mode)), they are optimal for use in differentchannel/link quality regions. A link adaptation algorithm selects themost efficient mode, over varying channel conditions, based, forexample, on a mode selection criterion. Therefore, in poor channelconditions, modes are selected to enable communication thus rendering arobust system. Under good channel conditions, spectrally efficient modesare selected to increase throughput. Similarly, the link adaptation issub-optimal under adverse conditions, such as if the receiver is movingat high vehicular speeds. Systems with no link adaptation or sub optimallink adaptation are constrained to use transmission modes designed tomaintain acceptable performance when the channel quality is poor to getmaximum coverage. Hence, these systems are effectively designed for theworst-case channel conditions, resulting in insufficient utilization ofthe full channel capacity.

Further, link adaptation is either unproven or incapable of robustoperation under adverse environments, for example, under high vehicularspeeds wherein the channel ages very rapidly. More specifically, thegrowing popularity of MIMO creates the need for link adaptationsolutions in adverse environments that integrate temporal, spatial, andspectral components. By providing an improved feedback by including thereceiver type, this sub-optimal link adaptation is offset in variousembodiments. In various embodiments, the present invention establishes atechnique for link adaptation that is robust, low in complexity andprovides cost effective procedures for future wireless systems.

A wireless communication system using a MIMO based wireless system isfirst described using FIG. 1, in accordance with an embodiment of thepresent invention. An embodiment of the invention using the adaptationis illustrated in FIG. 2. Embodiments of the invention for closed andopen loop MIMO are next described using FIGS. 4-5. Application ofembodiments of the invention to illustrate the significance of thereceiver type is described in FIG. 6.

FIG. 1 is a block diagram illustrating a wireless communication systemusing a MIMO based wireless system, in accordance with an embodiment ofthe invention. A compressed digital source in the form of a binary datastream 2 is fed to a simplified transmitting block 5 encompassing thefunctions of error control coding and (possibly joined with) mapping tocomplex modulation symbols. The simplified transmitting block 5 producesseveral separate symbol streams which range from independent topartially redundant to fully redundant. Each of the symbol streams ismapped to one of the multiple transmitter antennas 12-18. Mapping mayinclude linear spatial weighting of the antenna elements or linearantenna space-time precoding. After upward frequency conversion,filtering and amplification, the transmitting signals 6 are launchedinto the wireless channel by the multiple transmitters 12-18. At thereceiver 20, the signals are captured by possibly multiple antennas.Subsequently, demodulation and demapping operations are performed in areceiver unit 25 to recover the message. The level of intelligence,complexity, and a priori channel knowledge used in selecting the codingand antenna mapping algorithms are adapted by the receiver unit 25during the link 1 depending on the application and the nature of thetransmission.

The wireless transmitter 10 communicates with the receiver 20 via thewireless channel 21. The wireless transmitter 10 comprises four transmitantennas 12, 14, 16, and 18 and the receiver 20 comprises four receiveantennas 22, 24, 26, and 28. In other embodiments, any number oftransmit antennas and any number of receive antennas are used to form aMIMO channel. The wireless link 1 utilizes either closed loop or openloop MIMO techniques. The transmitter 10 dynamically tailors thetransmit signals 6 to the channel in a manner that improves channelthroughput or minimizes bit error rate or both. For example, atransmitted signal 6 is transmitted simultaneously through all the fourtransmit antennas 12, 14, 16, and 18 by decomposition into independent ¼rate bit sequences. If the transmit signals 6 from each of thetransmitters 12, 14, 16 and 18 into the channel are different from eachother, there will be a four-fold increase in the channel capacity. Ifthe transmit signals 6 from each of the transmitters 12, 14, 16 and 18into the channel are identical, there will be four-fold increase in thediversity.

In various embodiments, the receiver 20 transmits channel-relatedfeedback information to the transmitter 10 for use by the signalprocessing block 5 in developing transmit signals 6. The receiver 20generates the feedback information by, for example, appropriatelyprocessing reference information received from the transmitter 10. Invarious embodiments, the receiver 20 combines the reference informationgenerated by the transmitter with knowledge of the receiver's type andcharacteristics in generating the feedback information. In oneembodiment, for example, the present invention solves the problem ofsub-optimal link adaptation by taking the receiver type intoconsideration. Different types of receivers perceive a different channelquality. Hence, in various embodiments, the present invention includesthe type of receiver in defining the channel quality.

In various embodiments, the receiver comprises a linear receiver or anon-linear receiver. For example, a linear receiver can be a MMSE(minimum mean square error) in one embodiment. Similarly, a non-linerreceiver comprises an ML (maximum likelihood), or some other variant, invarious embodiments.

Assuming that there are M_(t) antennas at the transmitter 10 and M_(r)antennas at a receiver 20, there are M_(t)×M_(r) MIMO channels betweenthe transmitter 10 and the receiver 20. Let Y denote the signal receivedby antenna 22 on the receiver 20, X the signal transmitted by thetransmitter 20, and V the channel noise received by antenna 22 on thereceiver 20. The received signal Y is represented as:

Y=HX+V,  (1)

where H is the channel matrix observed by the signal in the wirelessmedium 21.

Channel matrix H, in turn, determines the performance of the MIMOnetwork. In wireless systems, the channel is typically random, i.e., His a random matrix. Common random matrix models for channels includeuncorrelated Rayleigh fading (i.e. the entries of H are independent andidentically distributed complex normal random variables), correlatedRayleigh fading, uncorrelated Rician fading, and correlated Ricianfading.

The correlations among the signals received by the receiving antennasdepend on the channel conditions and the degree of correlationsdetermines the rank of channel matrix H. The rank R of the MIMO channelis the number of independent equations offered by the above mentionedlinear system. It is also equal to the algebraic rank of the channelmatrix H. Hence, the rank is always both less than the number ofantennas on the transmitter 10 and less than the number of antennas onthe receiver 20. Consequently, the number of independent signals thatsafely transmits through the MIMO system is at most equal to the rank.

In an environment with severe multipath effects, the signals received byevery receiver antenna 22, 24, 26, and 28 on the receiver 20 are highlyuncorrelated. Consequently, MIMO channel matrix H has a high rank. In agood environment, for example, if the receiver 20 is in theLine-Of-Sight (LOS) range, the signals received by every receiverantenna 22, 24, 26, and 28 on the receiver 20 are highly correlated. Asa result, MIMO channel matrix H has a lower rank. The lowest rank forchannel matrix H is one wherein all signals received by differentantennas are correlated.

Improvements in performance of the MIMO network demand a channel matrixH with a high rank, and good signal to noise ratio. Both the number oftransmitted streams and the bit error rate on each stream determine thelink's efficiency (error free fraction of the conventional physicallayer throughput per transmitter antenna times number of transmittingantennas) rather than just the number of independent input streams.Since the use of coding on the multi-antenna signals (space-time coding)has a critical effect on the bit error rate behavior, it becomes animportant component of MIMO design. In various embodiments, the errorrate is reduced by improved modeling of the wireless communicationsystems. The receiver 20 feeds back to the transmitter 10 informationnot only regarding the wireless channel 21, but also informationregarding the receiver and/or subsequent processing such as decoding,etc. Including such detailed information regarding the receiver 20allows an improved tradeoff between coding and multiplexing, thusmaximizing the throughput rate of the wireless communication system.

A schematic of the link adaptation process is illustrated in FIG. 2, inaccordance with an embodiment of the present invention. This exampleprovides user end (UE) procedures in support of link adaptation in3GPP-LTE. 3GPP LTE (Long Term Evolution) is the name given to a projectwithin the Third Generation Partnership Project to improve the UniversalMobile Telecommunications System (UMTS) to cope with futurerequirements.

FIG. 2 illustrates a schematic embodiment of a wireless communicationsystem that utilizes aspects of the present invention. A base station(Node B) 110 communicates with user equipment or user unit receiver 120,which may be a mobile telephone, computer, laptop, hand held device, orother such device. As illustrated in FIG. 2, the Node B 110 sendsreference signal 101 to the user unit receiver 120. The user unitreceiver 120 estimates the channel information through the referencesignal 101 sent by Node B 110. The user unit receiver 120 generates afeedback signal 103, for example, by appropriately processing thereceived reference signal 102. The feedback signal 103 comprisesmodulation and coding scheme and rank. If a closed loop feedback isused, the feedback signal 103 also comprises precoding matrix indices.The user unit receiver 120 performs a signal to interference and noiseratio (SINR) 130 calculation. Subsequently, the user unit receiver 120uses this signal to interference and noise ratio (SINR) 130 along withthe receiver information 150 (e.g., type of receiver) to generate amodulation and coding scheme 140. A modulation and coding scheme 140thus generated is thus adapted to enhance the throughput of the channelfor the given characteristics of the receiver. Examples of modulationschemes include quaternary PSK (QPSK), 16-quadrature amplitudemodulation (16QAM), 64-quadrature amplitude modulation (64QAM), binaryphase shift keying (BPSK), etc. However, in some embodiments, thefeedback information 103 may comprise intermediate state information,for example, SINR and receiver type that can be processed, for example,by the base station to yield a rank and modulation and coding scheme.

In various embodiments, if the signaling protocol specifies that rankand MCS are to be fed back to the base station, the user unit receiver120 selects the rank and MCS based on SINR and the receiver type. If thereceiver type is not known to the base station, the base station shouldnot override the user unit receiver 120 recommendation, or else the basestation runs the risk of sub-optimal link adaptation. Or, for example,if the user unit receiver 120 is only expected to feedback SINR to thebase station, then the base station should know the receiver typethrough signaling. In some embodiments, the receiver types are agreedupon a priori. In other words, the idea behind various embodiments ofthe invention is the use of SINR and receiver type to guide linkadaptation, rather than SINR only.

In various embodiments, the signal to interference and noise ratio 130calculation, the receiver information 150, and the modulation and codingscheme 140 are added as additional code or by modifying existing code,for example, by suitable modification of the firmware (software) of theuser unit receiver 120. In some embodiments, some or all of thefunctionality requires separate hardware inside the user unit receiver120.

FIG. 3 illustrates 3GPP-LTE MIMO modes used in embodiments of thepresent invention. The user unit receiver 120 is configured by Node-B110 for different MIMO modes. The MIMO modes supported include bothtransmit diversity and spatial multiplexing modes. Transmit diversity isachieved by using space-time codes that introduce temporal and spatialcorrelation into signals transmitted from different antennas in order toprovide diversity at the receiver, and coding gain over an uncodedsystem without impairing bandwidth efficiency. Transmit diversity isused to offset transmission loss effects including effects arising fromfading during the propagation in the channel. Examples of transmitdiversity coding include space frequency block coding (SFBC), space timeblock coding (STBC), space-time trellis coding (STTC). In spatialmultiplexing, a signal is divided into multiple layers or streams, andtransmitted in parallel, increasing the throughput of the transmission.In various embodiments, the standard modes supported in 3GPP-LTE areused, although in other embodiments more or less transmission modes areused.

In one embodiment, the MIMO modes in closed loop transmission comprisetwo modes: a zero delay mode and a large delay mode. Each mode isdynamically configured and assigned a coding scheme based on the rank ofthe transmission. Closed loop MIMO in various embodiments comprises twomodes: a spatial multiplexing mode (e.g., WcDU) and a transmit diversitymode. The transmit diversity mode uses either space frequency blockcoding (SFBC) or SFBC and frequency switched transmit diversity (FSTD).These modes are semi-statically configured and signaled by Layer-3 (L3)signaling. As the transmitters are fixed, the vehicular speed of thereceiver defines the delay, and thus the ageing of the channel stateinformation. To improve the performance of an open loop transmission, acyclic precoding matrix index scheme may be used that cyclically selectsa codeword.

The UE feedback procedure for closed loop will now be described. FIG. 4illustrates an embodiment of the invention applied to closed loop MIMOsystems. Closed-loop MIMO comprises optimizing MIMO networks usingchannel state information at the transmitter to customize thetransmitted waveforms to provide higher link capacity and throughput.Closed-loop MIMO enables channel-aware scheduling for multiple users,and simplifies multi-user receivers by avoiding interference, andprovides a simple and general means to exploit spatial diversity. Aconsequence of using multiple antennas, however, is an increase in thenumber of channel state parameters. Channel state information needs tobe quantized and sent to the transmitter over a limited-rate feedbackchannel.

In closed MIMO, single-user linearly precoded space-time block codes aredescribed by the input/output relationship:

Y=HFS+V,  (2)

where, F is an M_(t)×M precoding matrix, S is an M×T space-time blockcodeword, and V is an M_(r)×T noise matrix. The precoder parameter M ischosen so that M≦M_(t). The space-time block codeword S (whether it bespatial multiplexing, orthogonal space-time block coding, etc.) isgenerated independent of the channel. The precoder is chosen using afunction f that maps an M_(r)×M_(t) channel realization to an M_(t)×Mprecoding matrix with F=f(H). The precoding matrix F adapts thetransmitted signal to the current channel conditions. In MIMO, theoptimal choice of the precoding matrix F is the right singular vectorsof the channel matrix H.

Referring to FIG. 4, a transmitter Node B 110 transmits a referencesignal 101 to the user receiver 120. A rank and PMI generator 129 in theuser end receiver 120 observes a channel realization by sampling areceived reference signal 102, to select the best precoding matrix F tobe used at the moment. For computational efficiency and to minimizefeedback complexity, the precoding matrix F may be stored as a precodingmatrix index. The precoding matrix index indicates the index of anelement that is closest to the precoding matrix F in a predefined arrayof matrices.

In various embodiments, the user end receiver 120 comprises a receivertype, for example, a non-linear maximum likelihood (ML) or a linearminimum mean square error (MMSE) receiver. Maximum likelihood receiversrequire the user end receiver 120 to consider all possible precodingmatrix F (or precoding matrix index) before making the decision, andhence can be computationally expensive. The linear minimum mean squareerror, although sub-optimal, is simpler. For example, for the minimummean square error receiver, the mean square error at the output of theuser end receiver 120 is a function of the precoding matrix F used atthe transmitter Node B 110.

Using the precoding matrix F (or precoding matrix index), the channelquality for each layer is determined. Typically, channel stateinformation (CSI) is measured using signal to interference and noiseratio (SINR). As the precoding matrix F is available, the SINRcalculated is the instantaneous signal to noise ratio for each spatiallayer. For example, in a minimum mean square error receiver, a weightedmean square error design, giving different weights to different receivedsignal streams yields different criteria, such as maximum rate and SINRfor each layer.

The user end receiver 120 comprises an MCS generator that utilizes theSINR and type of user end receiver 120 to select a modulation and codingscheme (MCS), e.g., MCS=f_(receiver) _(—) _(type)(SINR), wheref_(receiver) _(—) _(type) is a lookup table that maps SINR to MCS foreach layer. In various embodiments, for each type of user end receiver120, the MCS generator comprises a mapping of SINR against MCS for eachlayer. Different MCS schemes have different bit or frame error rates atthe same SINR. Hence, for a given SINR, a MCS scheme is adopted that iswithin a particular error rate while maximizing throughput. Hence, theMCS scheme selected for the same SINR and layer can be different for twodifferent types of receiver. Similarly, in different embodiments, thelook up table comprises other properties of a receiver. In variousembodiments, the SINR estimation may be impacted by channel estimationalgorithm. For example, the averaging schemes used to calculate PMI andMCS are performed over a group of sub-carriers and hence impact the SINRestimation.

The MCS scheme, rank and precoding matrix F (e.g. codebook) are sentback as feedback information 103 through the designated feedback channelto the transmitter. Transmitting the precoding matrix F, althoughaccurate, can take up valuable bandwidth. Instead, there are two mainapproaches to designing feedback: quantizing the channel or quantizingproperties of the transmitted signal. A vector quantizer works bymapping a real or complex valued vector into one of a finite number ofvector realizations. The mapping is designed to minimize some sort ofdistortion function such as the average mean squared error (MSE) betweenthe input vector and the quantized vector. Alternately, quantizedinformation needed to adapt the transmitted signal to current channelcondition is used. In such techniques, a codebook comprising a set ofmatrices is first generated.

Hence, the best precoding matrix F is stored as an index of the optimalcodeword to save bandwidth, so that only the codeword is transmitted.The user end receiver 120 sends a feedback signal 103 to the transmitterNode B 110. The feedback signal 103 comprises feedback information suchas the rank, modulation and coding schemes, and the precoding matrixindex. The transmitter Node B 110 uses the feedback information, forexample, the precoding matrix index, to generate a precoding matrix F.The transmitter Node B 110 applies the feedback information in thefeedback signal 103 to the subsequent transmitted signal. In variousembodiments, the transmitter Node B 110 does not override the rank orprecoding matrix indices selected by the user end receiver 120.

An open loop UE feedback procedure will now be described. The closedloop precoding scheme works well for low mobility user ends where thechannel variation is slow. However, the performance of closed loopprecoding schemes degrades rapidly with an increase in user endmobility. For medium and high mobility user ends, open loop schemes maybe more preferred. Medium mobility refers to user end velocities greaterthan about 30 km/hr. Unlike closed loop schemes, in open loop schemesthere is no feedback of channel state information.

In this procedure, the user end calculates the average SINR 130 based onthe estimated channel information. Unlike closed loop schemes, validchannel information is not available in open loop schemes. Hence, incontrast to closed loop schemes, in open loop schemes, the SINR 130 is afrequency, time and spatial average per receiver antenna. TransmitterNode B 110 transmits a reference signal 101 into the channel. Based on areceived reference signal 102, the user end receiver 120 estimates thechannel and calculates the effective SINRs for each rank. In the absenceof a channel matrix, in open loop schemes, the rank is the number ofindependent data streams. This is unlike a closed loop scheme where therank is the numerical rank of the matrix.

The rank and modulation and coding scheme 140 are selected togetherbased on the average SINR. The user end receiver 120 calculates themodulation and coding schemes 140 for each transmission and for eachrank (MCS_(rank)) according to the average SINR 130 and receiverinformation 150, e.g., MCS_(rank)=f_(receiver) _(—) _(type)^(rank)(SINR). In different embodiments, the MCS_(rank) is calculatedfrom a table look up that comprises average SINR, rank and receivertype. A single MCS and rank are selected that maximizes data through putrate. In various embodiments, an MCS selector 151 at the user endreceiver 120 selects the single MCS and rank that maximize thethroughput rate as:

Throughput=rank×MCS_(rank).

The user end receiver 120 feedbacks the rank of the transmission totransmitter Node B 110 along with the average SINR calculated for thatrank. In various embodiments, the throughput rate of the wireless systemincreases relative to a sub-optimal link adaptation scheme that does notuse adaptation based on receiver type. The increase in time delay athigh vehicular speeds can be partially offset by a reduction in thetransmission delay, effectively improving the quality of thecommunication despite the fast channel ageing.

FIGS. 6 a, 6 b and 6 c illustrate the performance of a system thatutilizes embodiments of the present invention. FIG. 6 a illustrates thetransmission schemes plotted in FIGS. 6 b and 6 c, FIG. 6 b illustratesthe performance of MIMO modes with a minimum mean square error receiver.FIG. 6 c illustrates the performance of MIMO modes with a maximumlikelihood receiver.

FIG. 6 a tabulates the various transmission schemes used in thesimulations of FIGS. 6 b and 6 c. Each of the simulated transmissionscheme is tabulated as a curve that corresponds to the curves on FIGS. 6b and 6 c. Referring to FIG. 6 b, the frame error rate plotted againstSINR for varying MCS schemes for a minimum mean square error receiver.The simulations assume ideal channel information using a SCM channelmodel for a UE traveling at high vehicular speeds (120 km/h). Asillustrated in FIG. 6 b, curve 4 illustrating a rank-1 transmissionusing space frequency block coding SFBC and frequency switched transmitdiversity SFBC performs better than curve 6 illustrating a rank-2transmission using WcDU.

However, in contrast, similar simulations for a maximum likelihoodreceiver yield the opposite result. In the particular case studied inFIG. 6 c, at high vehicular speeds, for the ML receiver, curve 6 therank-2 transmission using WcDU out performs any rank-1 transmissionusing SFBC-FSTD coding; for example, see curve 4 in FIG. 6 c. Hence, awireless communication system transmitting without the knowledge of thereceiver performs sub-optimally.

Although embodiments of the present invention and their advantages havebeen described in detail, it should be understood that various changes,substitutions and alterations can be made herein without departing fromthe spirit and scope of the invention as defined by the appended claims.For example, it will be readily understood by those skilled in the artthat many of the features, functions, processes, and materials describedherein may be varied while remaining within the scope of the presentinvention.

While this invention has been described with reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments, as well as other embodiments of the invention,will be apparent to persons skilled in the art upon reference to thedescription. It is therefore intended that the appended claims encompassany such modifications or embodiments.

1-25. (canceled)
 26. A system for link adaptation in MIMO systems,comprising: a receiver circuit of a linear receiver type or a non-linearreceiver type; a plurality of antenna ports configured to couple to aplurality of antennas, respectively, and to receive signals thereat,wherein the receiver circuit is configured to determine a signal tointerference and noise ratio (SINR) of one or more received signals; adeterminer logic configured to determine a mapping of signal tointerference and noise ratio (SINR) to modulation and coding scheme(MCS), wherein the mapping is dependent on the receiver type of thereceiver; and a selection logic configured to select a modulation andcoding scheme (MCS).
 27. The system of claim 26, where the linearreceiver type is a minimum mean square error (MMSE) receiver.
 28. Thesystem of claim 26, where the non-linear receiver type is a maximumlikelihood (ML) receiver.
 29. The system of claim 26, where the receivercircuit is located in a user equipment (UE).
 30. The system of claim 26,where the receiver circuit is located in a base station.
 31. The systemof claim 26, where the determiner logic is located in a UE.
 32. Thesystem of claim 26, where the determiner logic is located in a basestation.
 33. The system of claim 26, where the modulation and codingscheme (MCS) is one of QPSK, 16QAM, 64QAM, BPSK.
 34. The system of claim26, where the receiver circuit comprises at least two receiver types.35. The system of claim 26, where the signal to interference and noiseratio (SINR) information and the receiver type information aretransmitted via channel state information or channel qualityinformation.
 36. The system of claim 34, where the channel stateinformation includes rank information.
 37. The system of claim 26, wherethe received signals include reference signals.
 38. A system for linkadaptation in MIMO systems, comprising: a receiver circuit of a linearreceiver type or a non-linear receiver type; a plurality of antennaports configured to couple to a plurality of antennas, respectively, andconfigured to receive signals thereat, wherein the receiver circuit isconfigured to determine at least one signal to interference and noiseratio (SINR) of received signals; and a selection logic configured toselect a modulation and coding scheme (MCS) based on a mapping of signalto interference and noise ratio (SINR) to modulation and coding scheme(MCS), wherein the mapping is dependent on the receiver type of thereceiver.
 39. A system for link adaptation in MIMO systems, comprising:a receiver means of a linear receiver type or a non-linear receivertype; a plurality of antenna ports configured to couple to a pluralityof antennas, respectively, and configured to receive signals thereat,wherein the receiver means is operable for determining at least onesignal to interference and noise ratio (SINR) of received signals; and aselection means for selecting a modulation and coding scheme (MCS) basedon a mapping of signal to interference and noise ratio (SINR) tomodulation and coding scheme (MCS), wherein the mapping is dependent onthe receiver type of the receiver.